Accelerating HunyuanVideo Inference with ParaAttention: A WaveSpeed Breakthrough
At WaveSpeed, we’re constantly pushing the boundaries of what’s possible in AI media generation. We’re excited to share how we’ve leveraged ParaAttention and other cutting-edge techniques to dramatically accelerate HunyuanVideo inference, making real-time video generation a reality.
The Challenge of Video Generation Models
While open-source video generation models like HunyuanVideo, CogVideoX, and Mochi have shown remarkable progress, inference speed remains a significant bottleneck for real-world applications. These models have demonstrated remarkable capabilities in generating high-quality videos from textual descriptions. However, despite these achievements, the field still faces substantial challenges. The computational complexity and memory requirements of these models pose significant hurdles for real-world applications, especially when generating high-resolution videos with numerous frames. This has limited the widespread adoption and practical utility of AI video generation technologies in industries where real-time performance is crucial.
Our Solution: ParaAttention and Beyond
Context Parallelism and First Block Cache
Our approach begins with Context Parallelism and First Block Cache (FBC), implemented through our ParaAttention library. These techniques allow us to:
- Cache transformer block outputs: By caching and reusing these outputs when the residual difference is small enough, we can skip entire denoising steps without compromising video quality.
- Parallelize inference: Using Context Parallelism, we can scale the inference process across multiple GPUs efficiently.
FP8 Dynamic Quantization
To further optimize both speed and memory usage, we’ve implemented FP8 dynamic quantization. This technique reduces the precision of model weights and activations while maintaining accuracy, allowing us to leverage NVIDIA GPUs’ 8-bit Tensor Cores for accelerated computations.
Results That Speak for Themselves
The impact of our optimizations is dramatic:
| GPU Type | Number of GPUs | Optimizations | Wall Time (s) | Speed up |
|---|---|---|---|---|
| NVIDIA L20 | 1 | Baseline | 3675.71 | 1.00x |
| NVIDIA L20 | 1 | FBCache | 2271.06 | 1.62x |
| NVIDIA L20 | 2 | FBCache + CP | 1132.90 | 3.24x |
| NVIDIA L20 | 4 | FBCache + CP | 718.15 | 5.12x |
| NVIDIA L20 | 8 | FBCache + CP | 649.23 | 5.66x |
With just 8 NVIDIA L20 GPUs, we’ve achieved a remarkable 5.66x speedup compared to the baseline configuration. This means generating a 129-frame, 720p video that previously took nearly an hour can now be produced in just over 10 minutes.
The WaveSpeed Advantage
What sets our approach apart is the combination of multiple optimization techniques in a cohesive solution:
- Comprehensive optimization: We don’t rely on a single technique but instead combine Context Parallelism, First Block Cache, and FP8 quantization for maximum effect.
- Scalability: Our solution scales efficiently across multiple GPUs, making it suitable for both single-GPU workstations and multi-GPU server environments.
- Quality preservation: Despite the aggressive optimizations, we maintain near-perfect video quality, ensuring that speed doesn’t come at the cost of content integrity.
This breakthrough in video generation speed opens up new possibilities for real-time applications across various industries, from entertainment to advertising and beyond. At WaveSpeed, we’re committed to continuing this innovation, exploring new optimization techniques, and pushing the boundaries of what’s possible in AI-driven video creation.
Stay tuned for more updates on our journey to make AI video generation faster, more efficient, and more accessible to everyone.
Source of content: fastest_hunyuan_video.md
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